{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f8ba3161",
   "metadata": {},
   "source": [
    "# Interpretability 4: Feature attribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6535c1f2",
   "metadata": {},
   "source": [
    "How to determine the importance of features? This is known as feature attribution. This notebook shows how to get feature scores in KANs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d88fa9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n",
      "checkpoint directory created: ./model\n",
      "saving model version 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "| train_loss: 8.00e-03 | test_loss: 8.47e-03 | reg: 4.61e+00 | : 100%|█| 40/40 [00:07<00:00,  5.20it"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from kan import *\n",
    "from sympy import *\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(device)\n",
    "\n",
    "# let's construct a dataset\n",
    "f = lambda x: x[:,0]**2 + 0.3*x[:,1] + 0.1*x[:,2]**3 + 0.0*x[:,3]\n",
    "dataset = create_dataset(f, n_var=4, device=device)\n",
    "\n",
    "input_vars = [r'$x_'+str(i)+'$' for i in range(4)]\n",
    "\n",
    "model = KAN(width=[4,5,1], device=device)\n",
    "model.fit(dataset, steps=40, lamb=0.001);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36296de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c782f62",
   "metadata": {},
   "source": [
    "get feature score (for input variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2693a8c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.8916, 0.5155, 0.1079, 0.0040], device='cuda:0',\n",
       "       grad_fn=<MeanBackward1>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.feature_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fb3a0a8",
   "metadata": {},
   "source": [
    "Inspect how hidden nodes depend on features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2f80a6e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([0.8915, 0.5146, 0.1079, 0.0040], device='cuda:0',\n",
       "       grad_fn=<SelectBackward0>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the 2nd neuron (index start from 0) in the 1st layer\n",
    "model.attribute(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a297860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4.6616e-05, 8.2072e-04, 3.2453e-06, 1.3511e-05], device='cuda:0',\n",
       "       grad_fn=<SelectBackward0>)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the 3nd neuron (index start from 0) in the 1st layer\n",
    "# note the y axis scale is really small\n",
    "model.attribute(1,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6182005a",
   "metadata": {},
   "source": [
    "prune inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cac3ea5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keep: [True, True, True, False]\n",
      "saving model version 0.2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x400 with 27 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = model.prune_input()\n",
    "model.plot(in_vars=input_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e7eaa42",
   "metadata": {},
   "source": [
    "Let's consider a high-dimensional case. In the case of many inputs but only few are important, the users may want to prune input otherwise too many inputs make interpretable hard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a5b6ccf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checkpoint directory created: ./model\n",
      "saving model version 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "| train_loss: 3.20e-02 | test_loss: 5.46e-02 | reg: 1.71e+01 | : 100%|█| 50/50 [00:16<00:00,  3.12it"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 0.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from kan import *\n",
    "\n",
    "# let's construct a dataset\n",
    "n_var = 100\n",
    "\n",
    "def f(x):\n",
    "    y = 0\n",
    "    for i in range(n_var):\n",
    "        # exponential decay\n",
    "        y += x[:,[i]]**2*0.5**i\n",
    "    return y\n",
    "        \n",
    "dataset = create_dataset(f, n_var=n_var, device=device)\n",
    "\n",
    "input_vars = [r'$x_{'+str(i)+'}$' for i in range(n_var)]\n",
    "\n",
    "model = KAN(width=[n_var,10,10,1], seed=2, device=device)\n",
    "model.fit(dataset, steps=50, lamb=1e-3);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd91e538",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 1132 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "eefc4650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rewind to model version 0.1, renamed as 1.1\n"
     ]
    }
   ],
   "source": [
    "model = model.rewind('0.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3e42f8d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'feature attribution score')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(n_var)+1, model.feature_score.cpu().detach().numpy())\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('rank of input features', fontsize=15)\n",
    "plt.ylabel('feature attribution score', fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bf0deb1",
   "metadata": {},
   "source": [
    "Since there are 100D inputs, it's very time consuming to plot the whole diagram and hard to read anything meaningful out of the diagram. So we want to prune the network first (including pruning hidden nodes and pruning inputs) and then plot it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e0b3dad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saving model version 1.2\n",
      "keep: [True, True, True, True, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]\n",
      "saving model version 1.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x600 with 28 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = model.prune()\n",
    "model = model.prune_input(threshold=3e-2)\n",
    "model.plot(in_vars=input_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42003070",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
